Transcript GAQS Talk - Georgia Institute of Technology

Aerosol Indirect Effect:
The elusive component of climate change
Athanasios Nenes
School of Earth and Atmospheric Sciences
School of Chemical and Biomolecular Engineering
Georgia Institute of Technology
photo: S.Lance
Georgia Air Quality & Climate Summit, May 4, 2006
Humans affect clouds and hydrological cycle?
Yes! By changing the concentration of Cloud Condensation
Nuclei (CCN) in the atmosphere. This phenomenon is known as
the “indirect climatic effect of aerosols”.
Higher Albedo
Lower Albedo
CCN
Clean Environment
(few CCN)
CCN
Polluted Environment
(more CCN)
Increasing particles tends to cool climate (potentially alot).
Climate models are the only tools for assessing the
anthropogenic indirect effect, but predictions are subject to
very large uncertainty.
Observational evidence of indirect effect
“Ship tracks”: linear features of high cloud reflectivity
embedded in marine stratus clouds, resulting from
aerosols emitted by ships.
Ship plume
incorporated
into cloud
Ship
Track
M.Kulmala: “Nucleation and Atmospheric Aerosols, 1996”
Anthropogenic indirect effect: important & elusive
IPCC (2001)
How important is the indirect effect in Georgia?
Problems with Computer Models
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Cloud formation happens at smaller spatial scales
than global climate models can resolve, and must be
parameterized.
Aerosol-cloud interactions are complex; many
processes are poorly represented, constrained
and/or understood.
Climate models provide limited information about
clouds and aerosols.
Quantifying the aerosol indirect effect requires a
relationship between aerosol and cloud droplet
number concentration. Empirical relationships
often used.We need to “get away” from this.
Solution: Introduce as much physics as possible
Dynamics
Updraft Velocity
Large Scale Thermodynamics
Particle characteristics
Size
Concentration
Chemical Composition
collision/coalescence
Cloud Processes
Cloud droplet formation
Drizzle formation
Rainwater formation
Chemistry inside cloud droplets
drop growth
activation
particles
“Population” links: Aerosol - CCN – Droplets – Drizzle
All the links need to be incorporated in global models.
Cloud drop formation in GCMs: using first principles
Advantages
t
• Explicit representation of aerosol
chemistry and size distribution.
• Explicitly calculate droplet number
and size distribution based on first
principles.
drop growth
Smax
activation
• Chemically complex and externally
mixed aerosol can be treated.
aerosol
S
Implications
• Much slower than empirical relationships.
• Need for subgrid cloud dynamics (updraft velocity). These quantities
are not explicitly resolved by GCMs and must be parameterized.
• Detailed information on aerosol size distribution and chemical
composition is needed.
Nenes and Seinfeld (2003) cloud formation
relationship
Input: P,T, vertical wind, particle characteristics.
Output: Droplet number & size distribution, Smax
How:
Solve an algebraic equation.
 wGSmax
2
aV
S part
S max




C1  f1 ( s)ds  C2  f 2 ( s)ds  1  0


0
S part


Features:
- 103 times faster than full numerical cloud model.
- can be implemented in Global Climate Model.
- can treat complex chemical composition.
- in-situ validation for a wide range of airmass and cloud
types (Meskhidze et al., JGR; Fountoukis et al., in review)
Aerosol Indirect Effect:
How do we estimate it?
We use a global climate model (GCM)
• simulation with current day emissions
• simulation without anthropogenic emissions
(“preindustrial” scenario)
• compute cloud droplet number, optical depth
and change in radiation from the aerosol-cloud
interactions (“indirect forcing”)
• compare annual average forcing to greenhouse
gas warming (~ 2.5 W m-2)
• Net forcing (greenhouse + indirect) is used as a
proxy for climate change.
Modeling the Indirect Effect
Global Model #1
NASA Global Modeling Initiative (GMI)
http://gmi.gsfc.nasa.gov/gmi.html
• 3-D chemistry-transport model (CTM)
• Multiple “packages” for e.g., chemistry & aerosol
• Metrological inputs from GCMs (GEOS-4 FVGCM &
GISS-II’) or data assimilation systems (NASA DAO)
• Any vertical resolution; horizontal resolutions of
1°x1.25°, 2°x2.5°, or 4°x5°
• Multi-year assessment simulations
Modeling the Indirect Effect
Global Model #2
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•
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NASA GISS II’ GCM (fully-coupled climate model)
4’5’ horizontal resolution
9 vertical layers (27-959 mbar)
Aerosol Microphysics
•
The TwO-Moment Aerosol Sectional (TOMAS)
microphysics model (Adams and Seinfeld, JGR,
2002) is applied in the simulations.
•
Two moments of the size distribution (mass and
number) are tracked and conserved for each size
bin in the microphysical processes of coagulation,
condensation/evaporation and nucleation.
•
A bulk microphysics version is also available & used.
Modeling Framework
Emissions
•
IPCC scenarios (current day, preindustrial)
Tracers
•
•
•
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Model includes 30 size bins from 10 nm to 10 m.
For each size bin, model tracks:
– Aerosol number
– Sulfate mass
– Sea-salt mass
Gas-phase species: H2O2, SO2, DMS and H2SO4
Cloud microphysical parameters:
– Droplet number
– Effective radius
– Optical depth
Modeling Framework
In-cloud updraft velocity
Cloud-base updraft velocity is necessary to calculate
droplet number. GCMs cannot resolve this and must be
parameterized as well. Approaches considered:
• Prescribed (marine: 0.3-0.5 ms-1; continental: 0.5-1 ms-1).
• Diagnosed from large-scale TKE resolved in the GCM.
Spectral width determined from scaling arguments or
observations.
• We also use an alternative proposed by Lance et al.,
JGR, (2004). This uses a combination of empirical
aerosol-cloud droplet correlations and a parcel model to
infer a “basecase” updraft velocity.
Cloud Droplet Number: Major features
North American
pollution plumes
Long-range transport
Biogenic
emissions
European and Asian
pollution plumes
(cm-3)
Cloud droplet number
Change from Preindustrial to Present
Conditions
• GISS II’ GCM
• Water vapor accommodation coefficient = 0.042
CDNC (cm-3)
CDNC (%)
Annual average indirect forcing
Global annual average ~ -1 Wm-2
Georgia annual average ~ -4 Wm-2
Aerosol Indirect Forcing:
Estimating its uncertainty
How sensitive are estimates of forcing to the:
• aerosol microphysics used?
• GCM wind fields?
• cloud updraft velocity?
• errors from application of cloud droplet
formation theory?
• poorly constrained thermokinetic parameters?
Interested in global distributions but focus on Georgia
Aerosol Indirect Effect:
Sensitivity to accommodation coefficient
Conditions
• GISS II’ GCM
• Accommodation coefficient range (1.0 - 0.01)
CDNC (a change)
Global
Georgia
~ 40 cm-3
~ 250 cm-3
CDNC (Present-Preind)
~ 100 cm-3
~ 750 cm-3
Aerosol Indirect Effect:
Uncertainty from errors in theory.
Forcing
autoconversion
Global
~ 0.5 W m-2
~ 20 %
Georgia
~ 0.5-0.8 W m-2
~ 25-35 %
Indirect Effect: Sensitivity to met.field
GEOS-4
DAO
Global annual average:
-0.75 Wm-2 to -1.08 Wm-2
GISS-II’
Georgia annual average:
-4.0 Wm-2 to -3.5 Wm-2
SUMMARY
• Accurate parameterizations are being developed that
address key aerosol-cloud interactions at their
appropriate scale, and are linked together consistently.
• Complex compositional effects and aerosol
heterogeneity can for the first time be treated in
GCMs.
• By linking parameterizations at their appropriate
scale, future integration of additional interactions is
possible.
• A major issue is constraining chemical composition
information (especially for the organics) to quantities
available from GCM simulations. In-situ observations are
key for this.
SUMMARY
Global Indirect forcing:
• Annual average: -0.96 W m-2 (50% uncertainty)
Georgia Indirect forcing:
• Annual average: -4.0 W m-2 (50% uncertainty)
What does this mean?
On a global scale, warming from greenhouse gases is
stronger (consistent with global change phenomena).
In Georgia, indirect effect is equal or stronger than
greenhouse gas warming (consistent with minor
changes in local temperature). This will change in the
future with improved air quality and increased CO2
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ACKNOWLEDGMENTS
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People Level
Second
Nicholas
Meskhidze, Gatech
Third
Level
RafaellaLevel
Sotiropoulou, Gatech
Fourth
Christos
Fountoukis, Gatech
Fifth
Level
Jeessy Medina, Gatech
John Seinfeld, Caltech
Peter Adams, Carnegie Mellon
Robert Griffin, UNH
Laura Cottrell, UNH
photo: S.Lance
Funding
NSF CAREER
NASA
NOAA
Blanchard-Milliken Fellowship
GA Tech Startup
For more information and PDF reprints, go to
http://nenes.eas.gatech.edu
THANK YOU !